Method and system for determining olfactory perception signature

ABSTRACT

A method of determining olfactory perception signature of a subject is disclosed. The method comprises: providing the subject with a plurality of physical odorant samples for sniffing; for each sniffed odorant sample, presenting to the subject, by a user interface, a set of odorant descriptors and a respective set of rating controls, and receiving ratings entered by the subject using the rating controls. Each rating is indicative of a descriptiveness of a respective odorant descriptor for the odorant sample, thereby obtaining a set of descriptiveness levels for the odorant sample. The method also comprises calculating, by a computer, relations between pairs of sets of descriptiveness levels corresponding to pairs of odorant samples, to provide a vector of relations, wherein the vector represents the olfactory perception signature of the subject.

RELATED APPLICATIONS

This application is a division of U.S. patent application Ser. No.15/188,104 filed on Jun. 21, 2016.

The contents of the above application are all incorporated by referenceas if fully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to olfactoryperception and, more particularly, but not exclusively, to a method anda system for determining olfactory perception signature.

Odors are complex mixtures of chemical species, and so contain manyconstituent molecules. The biological olfactory system is a remarkablesensor having many olfactory cells or odorant receptors, but not verymany different types of olfactory cells. The characterization of a scentor odor is typically through the combined response of many of thereceptors.

Because any two individuals differ by ˜30% of their olfactory receptorsubtype genome, this renders a potentially unique nose for each person.If one could capture this uniqueness with a perceptual test, a sort ofperceptual olfactory fingerprint, this should then be informative on theunderlying individual olfactory receptor subtype genome. The notion of apsychophysical test informing on underlying genes is of course wellknown from vision where color blindness charts inform about genes codingfor different opsins in the retina.

U.S. Pat. No. 6,558,322 teaches methods and kits for determiningolfactory perception. A test person's olfactory perception is evaluatedand then determined by first providing the test subject with a paletteof varying odors and fragrances, and then having that person describe,in full detail, each scent sample.

Background art includes Milinski M & Wedekind C (2001) Behav Ecol12(2):140-149.

SUMMARY OF THE INVENTION

According to an aspect of some embodiments of the present inventionthere is provided a method of determining olfactory perception signatureof a subject. The method comprises: providing the subject with aplurality of physical odorant samples for sniffing; for each sniffedodorant sample, presenting to the subject, by a user interface, a set ofodorant descriptors and a respective set of rating controls, andreceiving ratings entered by the subject using the rating controls. Eachrating is indicative of a descriptiveness of a respective odorantdescriptor for the odorant sample, thereby obtaining a set ofdescriptiveness levels for the odorant sample. The method also comprisescalculating, by a computer, relations between pairs of sets ofdescriptiveness levels corresponding to pairs of odorant samples, toprovide a vector of relations, wherein the vector represents theolfactory perception signature of the subject.

According to some embodiments of the invention the method comprisesgenerating a graphical output describing the vector of relations.

According to some embodiments of the invention the method comprisesobtaining an olfactory perception signature of another subject andcomparing the olfactory perception signature of the subject with theolfactory perception signature of the other subject.

According to some embodiments of the invention the olfactory perceptionsignature of another subject is obtained by accessing a computerreadable database and selecting the olfactory perception signature ofthe other subject from the database.

According to some embodiments of the invention the method comprises,based on the comparison, determining likelihood for successfulrelationship between the subject and the other subject.

According to some embodiments of the invention the method comprises,based on the comparison, determining likelihood for Human leukocyteantigen (HLA) matching between the subject and the other subject.

According to some embodiments of the invention the comparison is by ametric selected from the group consisting of statistical correlation,Euclidian distance, Log-Euclidean distance, Angular distance,significance test distance, Chebyshev distance, Manhattan distance, andMinkowski distance.

According to some embodiments of the invention the method comprises:accessing a computer readable database, each entry of the databasehaving a database olfactory perception signature and annotationinformation; searching the database for a database olfactory perceptionsignature that is similar to the olfactory perception signature of thesubject; and extracting from the database annotation informationassociated with the similar database olfactory perception signature.

According to some embodiments of the invention each annotationinformation of the database is a personality trait, and the methodcomprises determining a psychological condition of the subject based onthe extracted annotation information.

According to some embodiments of the invention each of at least someannotation information of the database is selected from the groupconsisting of: openness to experience, conscientiousness, extraversion,agreeableness, and neuroticism.

According to some embodiments of the invention the method furthercomprises predicting an outcome of a psychological test for the subject,based on the extracted annotation information.

According to some embodiments of the invention the computer is remotefrom the user interface, and the method comprises transmitting the setof descriptiveness levels over a communication network to the computer.

According to an aspect of some embodiments of the present inventionthere is provided a method for matching members of an online community.The method comprises: providing to a member of the community a pluralityof physical odorant samples for sniffing. At a client computer:receiving sniffing ratings entered by the member using rating controlsof a user interface of the client computer, calculating an olfactoryperception signature of the member based on the ratings, andtransmitting the olfactory perception signature to a server computer. Atthe server computer: accessing a computer readable database having aplurality of database olfactory perception signatures of other membersof the community searching the database for a database olfactoryperception signature that is similar to the olfactory perceptionsignature of the member, and transmitting to the client computer anindication that a similar database olfactory perception signature hasbeen found.

According to some embodiments of the invention the method comprisesdisplaying on the user interface a set of odorant descriptors for eachodorant sample, wherein the sniffing ratings are indicative ofdescriptiveness of each odorant descriptor of the set.

According to some embodiments of the invention the calculation of theolfactory perception signature comprises calculating relations betweenpairs of sets of descriptiveness levels corresponding to pairs ofodorant samples.

According to some embodiments of the invention the calculation of therelations comprises, for each pair of odorant samples, averaging squareddifferences between descriptiveness levels of a first odorant sample ofthe pair, and respective descriptiveness levels of a second odorantsample of the pair.

According to an aspect of some embodiments of the present inventionthere is provided a server system for communicating in a matchingservice for matching members of an online community. The server systemcomprises: a transceiver arranged to receive and transmit information ona communication network; and a processor arranged to communicate withthe transceiver, and perform code instructions, comprises: codeinstructions for receiving from a client computer an olfactoryperception signature of a member; code instructions for accessing acomputer readable database having a plurality of database olfactoryperception signatures of other members of the community; codeinstructions for searching the database for a database olfactoryperception signature that is similar to the olfactory perceptionsignature of the member; and code instructions for transmitting to theclient computer an indication that a similar database olfactoryperception signature has been found.

According to an aspect of some embodiments of the present inventionthere is provided a client system for communicating in a matchingservice for matching members of an online community. The client systemcomprises: a transceiver arranged to receive and transmit information ona communication network; and a processor arranged to communicate withthe transceiver, and perform code instructions, comprises: codeinstructions for displaying a set of rating controls on a userinterface; code instructions for receiving sniffing ratings entered by amember using the rating controls; code instructions for calculating anolfactory perception signature of the member based on the ratings; codeinstructions for transmitting the olfactory perception signature to aserver computer; and code instructions for receiving from the servercomputer an indication whether or not a matching member has been foundin a database, based on the transmitted olfactory perception signature.

According to some embodiments of the invention the processor is arrangedto display on the user interface a set of odorant descriptors,respectively corresponding to the set of rating controls, wherein thesniffing ratings are descriptiveness levels corresponding to the odorantdescriptors.

According to some embodiments of the invention the processor is arrangedto display the set of odorant descriptors and the a set of ratingcontrols a plurality of times, each times, and to receive the sniffingratings a respective plurality of times, thereby to obtain a pluralityof sets of descriptiveness levels, wherein the calculation of theolfactory perception signature comprises calculating relations betweenpairs of sets of descriptiveness levels.

According to some embodiments of the invention the calculation of therelations comprises, for each pair of sets, averaging squareddifferences between descriptiveness levels of a first set pair, andrespective descriptiveness levels of a second set of the pair.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIGS. 1A and 1B are schematic illustrations describing a technique forobtaining olfactory fingerprints, according to some embodiments of thepresent invention. Odorant ratings along visual-analogue scales (VAS)are converted into numbers reflecting location on the VAS line. Pairwiseodorant relation is calculated as the distance across all descriptorsused, and pairwise person similarity is calculated as the correlationacross odors. All this assures that fingerprints are odorant-specificbut descriptor-independent. For example, imagine John who was raised onan island smelling real coconuts, and Jane who knows coconut only fromBounty chocolate bars. “Coconut” is very different for these twoindividuals. John may rate Odor A as 47% like coconut, Odor B as 49%like coconut, and Odor C as 4% like coconut. Thus, odors A and B arehighly similar, and both are dissimilar from odor C. Jane may rate sameOdor A as 21% like coconut, Odor B as 19% like coconut, and Odor C as100% like coconut. Once again, odors A and B are highly similar, andboth are dissimilar from odor C. Thus, John and Jane will have verysimilar olfactory fingerprints derived from these three odorants and onedescriptor, even though they are in total disagreement as to whatcoconut smell is like.

FIGS. 2A-2G show olfactory fingerprints and their characterizations,according to some embodiments of the present invention. The fingerprintswere consistent within individuals and different across individuals. Tovisualize fingerprints, 378 pairwise similarities were interpolated. A)An example olfactory fingerprint of one individual. B) The olfactoryfingerprint of the same individual from A, but here derived using adifferent set of non-overlapping descriptors. C) The olfactoryfingerprint of the same individual from A and B, but here obtainedseparately 16 days later. D) The olfactory fingerprint of a differentindividual. Correlations: A Û B, r=0.89; A Û C, r=0.61; A Û D, r=0.25.E) The best-correlated descriptors across all odors. F) Heat-map matrixof distances between all subject pairs. G) Histogram of correlationcoefficients of all non self-self pairs.

FIGS. 3A-3B illustrate that olfactory fingerprints are independent ofdescriptor identity. A) Heat-map matrix of distances betweenfingerprints A and B for 89 subjects, where A and B were derived usingthe same odorants but different descriptors. The diagonal represents thecorrelation of a subject to him/herself. B) Violin plots comparingcorrelation coefficients of all self-self pairs (using differentdescriptors) to all self other pairs, the distribution of correlationcoefficients of self-self and self-others are shown in orange, the meanand median of the distribution are depicted in black and redrespectively.

FIGS. 4A-4D illustrate that fingerprints depend on the number of odorsand descriptors and the passing of time. A) Heat-map of fingerprintability to distinguish self-self from self-other pairs (represented inZ-Score values) as a function of number of odors and descriptors used.Dashed line represents Z-Score value of 1.65 (p=0.05). B) 3D plot ofZ-Score values as a function of number of odors and descriptors used togenerate a fingerprint. C) First test-retest. Violin plot comparingcorrelation coefficients of 23 subjects refingerprinted across time.Left side represents correlation coefficients distribution of a subjectto him/herself over time. Right side represents correlation coefficientsdistribution of a subject to other subjects over time. The mean andmedian of the distribution are depicted in black and red respectively.D) Second test-retest with five repetitions. Right Y axis is correlationacross retests (r) shown in yellow. Left Y axis is the ability of thefingerprint to discriminate self from others in Z-score values whencomparing the first to ensuing retests (black bars) or each twoconsecutive retests (red line).

FIGS. 5A-5B illustrate that similar olfactory fingerprints imply highHuman leukocyte antigen (HLA) matching. A) 16770 pairwise comparisons ofolfactory fingerprint distance vs HLA match. The dotted red linereflects the cutoff for saved tests. B) ROC curves of HLA comparisonssaved vs. HLA matches missed. The diagonal identity line reflects nogain or loss. ROCs: Red=using all 11 odors, Gray=200 testing curvesusing 4 odorants, Black and Blue=median and mean of the 4 best odorantsrespectively.

FIGS. 6A-6B are exemplary raw data showing the use of 54 descriptors inexperiment 1A across all subjects and all odors. Some descriptors wererated zero for some odors; however, all of the descriptors were ratedabove 80 for some odors and subjects. (A) Dot plot. (B) Violin plot.

FIGS. 7A-7B are exemplary raw data showing the use of 54 descriptors inexperiment 2 across all subjects and all odors. Some descriptors wererated zero for some odors; however, all of the descriptors were ratedabove 80 for some odors and subjects. (A) Dot plot. (B) Violin plot.

FIGS. 8A-8E. (A) An example of calculating the Z value for one subject.A Gaussian is fitted (magenta line) to the distribution of correlationcoefficients (CC) between subject's fingerprint A of and all othersubjects (blue bars). Z value of subject's CCs between fingerprint A andB (red bar) is calculated using the mean and SD obtained from the fittedGaussian. (B) Distribution of olfactory fingerprint CC. (Upper)Distribution of CC between a subject and all other subjects. (Lower)Distribution of CC between a subject and him/herself. (C) PERMANOVA testto compare intrasubject distance to intersubject distance within thesame session. Distribution of bootstrapped (flipped labels) PERMANOVApseudo F values (blue bars) compared with the real pseudo F values (redarrow). (D) PERMANOVA test to compare intrasubject distance tointersubject distance between sessions. Distribution of bootstrapped(flipped labels) PERMANOVA pseudo F values (blue bars) compared with thereal pseudo F values (red arrow). (E) Olfactory fingerprint distance vs.HLA match; 16,770 pairwise comparisons of olfactory fingerprint distance(calculated using CC) vs. HLA match value. Blue circles representsubject pairs with low (0-4) HLA match, green circles represent subjectpairs with high (5, 6) HLA match.

FIGS. 9A-9E. (A) An example of calculating the Z value for one subject.A Gaussian is fitted (magenta line) to the distribution of Euclidiandistances (ED) between subject's fingerprint A of and all other subjects(blue bars). Z value of subject's ED between fingerprint A and B (redbar) is calculated using the mean and SD obtained from the fittedGaussian. (B) Distribution of olfactory fingerprint ED. (Upper)Distribution of ED between a subject and all other subjects. (Lower)Distribution of ED between a subject and him/herself. (C) PERMANOVA testto compare intrasubject distance to intersubject distance within thesame session. Distribution of bootstrapped (flipped labels) PERMANOVApseudo F values (blue bars) compared with the real pseudo F values (redarrow). (D) PERMANOVA test to compare intrasubject distance tointersubject distance between sessions. Distribution of bootstrapped(flipped labels) PERMANOVA pseudo F values (blue bars) compared with thereal pseudo F values (red arrow). (E) Olfactory fingerprint distance vs.HLA match; 16,770 pairwise comparisons of olfactory fingerprint distance(calculated using ED) vs. HLA match value. Blue circles representsubject pairs with low (0-4) HLA match, green circles represent subjectpairs with high (5, 6) HLA match.

FIGS. 10A-10E. (A) An example of calculating the Z value for onesubject. A Gaussian is fitted (magenta line) to the distribution of logof Euclidian distances (LoED) between subject's fingerprint A and allother subjects (blue bars). Z value of subject's LoED betweenfingerprint A and B (red bar) is calculated using the mean and SDobtained from the fitted Gaussian. (B) Distribution of olfactoryfingerprint LoED. (Upper) Distribution of LoED between a subject and allother subjects. (Lower) Distribution of LoED between a subject andhim/herself. (C) PERMANOVA test to compare intrasubject distance tointersubject distance within the same session. Distribution ofbootstrapped (flipped labels) PERMANOVA pseudo F values (blue bars)compared with the real pseudo F values (red arrow). (D) PERMANOVA testto compare intrasubject distance to intersubject distance betweensessions. Distribution of bootstrapped (flipped labels) PERMANOVA pseudoF values (blue bars) compared with the real pseudo F values (red arrow).(E) Olfactory fingerprint distance vs. HLA match; 16,770 pairwisecomparisons of olfactory fingerprint distance (calculated using LoED)vs. HLA match value. Blue circles represent subject pairs with low (0-4)HLA match, green circles represent subject pairs with high (5, 6) HLAmatch.

FIG. 11 is a flowchart diagram of a method suitable for determiningolfactory perception signature of a subject, according to someembodiments of the present invention.

FIG. 12 is a schematic illustration of a collection of odorant samples,which can be used for determining olfactory perception signatureaccording to some embodiments of the present invention.

FIG. 13 is a schematic illustration of a client-server configurationwhich can be used for determining olfactory perception signatureaccording to some embodiments of the present invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to olfactoryperception and, more particularly, but not exclusively, to a method anda system for determining olfactory perception signature.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

FIG. 11 is a flowchart diagram of a method suitable for determiningolfactory perception signature of a subject, according to variousexemplary embodiments of the present invention. It is to be understoodthat, unless otherwise defined, the operations described hereinbelow canbe executed either contemporaneously or sequentially in manycombinations or orders of execution. Specifically, the ordering of theflowchart diagrams is not to be considered as limiting. For example, twoor more operations, appearing in the following description or in theflowchart diagrams in a particular order, can be executed in a differentorder (e.g., a reverse order) or substantially contemporaneously.Additionally, several operations described below are optional and maynot be executed.

At least part of the operations described herein can be can beimplemented by a data processing system, e.g., a dedicated circuitry ora general purpose computer, configured for receiving data and executingthe operations described below. At least part of the operations can beimplemented by a cloud-computing facility at a remote location. The dataprocessing system or cloud-computing facility can serve, at least forpart of the operations as an image processing system, wherein the datareceived by the data processing system or cloud-computing facilityinclude image data.

Computer programs implementing the method of the present embodiments cancommonly be distributed to users by a communication network or on adistribution medium such as, but not limited to, a floppy disk, aCD-ROM, a flash memory device and a portable hard drive. From thecommunication network or distribution medium, the computer programs canbe copied to a hard disk or a similar intermediate storage medium. Thecomputer programs can be run by loading the code instructions eitherfrom their distribution medium or their intermediate storage medium intothe execution memory of the computer, configuring the computer to act inaccordance with the method of this invention. All these operations arewell-known to those skilled in the art of computer systems.

The method of the present embodiments can be embodied in many forms. Forexample, it can be embodied in on a tangible medium such as a computerfor performing the method operations. It can be embodied on a computerreadable medium, comprising computer readable instructions for carryingout the method operations. In can also be embodied in electronic devicehaving digital computer capabilities arranged to run the computerprogram on the tangible medium or execute the instruction on a computerreadable medium.

Referring to FIG. 11 the method begins at 10 and optionally andpreferably continues to 11 at which the subject is provided with aplurality of physical odorant samples for sniffing.

Each odorant sample can contain an odorant component or an odorantmixture containing a plurality of odorant components.

As used herein, an “odorant component” is a monomolecular substancewhich can be sensed by the olfactory receptors and is perceived ashaving a smell in humans.

Optionally and preferably, one or more of the odorant samples is insolid state, but odorant samples in liquid or gaseous states are alsocontemplated. In various exemplary embodiments of the invention at least5 or at least 10 or at least 15 or at least 20 odorant samples areprovided. Preferably the number of odorant samples is less than 50. Thenumber of odorant samples is denoted below by M.

The M odorant samples can be provided in any form. For example, they canbe provided as a scratch-and-sniff stickers or cards, liftoff-and-sniffstickers or cards, sniff-jars, sealed absorbing pads, and the like. Acollection of odorant samples provided as scratch-and-sniff cards isillustrated in FIG. 12, showing an array 120 of eight scratch-and-sniffcards 122 each card containing an odorant component or an odorantmixture. It is appreciated that array 120 can include any number ofcards 122, and that the subject can be provided with one, or more thanone array 120.

Any odorant component or odorant mixture can be used for the odorantsample. A non-exhaustive list of possible odorant components or odorantmixtures includes, without limitation, root beer, cola, vanilla,chocolate, mint, peanut butter, apple, orange, grapefruit, peach,cinnamon, leather, ocean, burning rubber, cut grass, carrot, hard-boiledegg, butterscotch, strawberry, banana, blueberry, bubblegum, lavender,rose, pepper, clove, coffee, tea, tomato sauce, oregano, mustard, magicmarker, pumpkin pie, raspberry, lemon, vinegar, dill, pineapple, sourapple, almond extract, licorice, cotton candy, popcorn, cherry, pine,chicken noodle soup, macaroni and cheese, hot dog, ginkgo, olive, applepie, BBQ, birthday cake, candy corn, caramel, cheddar cheese, cherrypie, chili, fish, fresh bread, gingerbread, hamburger, pecan pie, hotdog, jelly bean, licorice, marshmallow, Mexican food, popcorn, pumpkinpie, roast beef, lemon lime, spaghetti, waffle, honey, root beer, spicedcider, apple, banana, blueberry, cherry, coconut, grape, green apple,lemon, lemonade, chocolate, chocolate mint, cola, cotton candy, peanutbutter, pie crust, pina colada, almond, cucumber, dill pickle,carnation, daffodil, gardenia, general floral, geranium, hay, hibiscus,honey suckle, lawn, lilac, lily, magnolia, mulberry, orchid, pine,spruce pine, rose, wheat, tulip, sunflower, violet, hyacinth, maple,blue spruce, basil, butterscotch, black pepper, cinnamon, clove, garlic,hazelnut, mesquite, airy fresh, band-aid, balsam, baby powder, bergamot,bubble gum, cigar, frankincense, perfume, soothing, leather, menthol,money, new car, soap, sea breeze, suntan oil, tobacco, tooth paste,campfire, invigorating, uplifting, ash tray, compost, manure, jasmine,cedar, pine, juniper, ginger, myrrh, truffle, chocolate chip cookies,pizza, anchovy, anise, and eucalyptus.

In experiments performed by the present inventors the following odorantsamples were used: moth ball, eucalyptus, strawberries, burnt rubber,sweat, natural gas, dill pickle, fish, cigar, manure, musk, ashtray,root beer, compost, green apple, cheese, mango, garlic, maple, anise,rose, blue spruce, clove, banana, banana (isoamyl acetate), eucalyptus(1,8-cineole), wet grass (cis-3-hexen-1-ol), and isovaleric acid.

Referring again to FIG. 11, at 12 the subject is presented with a set ofodorant descriptors for each sniffed odorant sample. The odorantdescriptors are optionally and preferably presented by a user interfacesuch as, but not limited to, a graphical user interface displayed on acomputer screen, a smart TV screen, or a screen of a mobile device,e.g., a smartphone device, a tablet device or a smartwatch device. Insome embodiments, a set of rating controls is also displayed, preferablyon the same screen.

The odorant descriptors are human-language descriptors and are presentedin a human-readable form to allow the subject to read and decipher them.Typically, each of the descriptors is associated with a known odor, notnecessarily odor that is emitted by one of the odorant samples, or asubjective perception of odor. For example, a descriptor can be atextual phrase, such as, but not limited to, “smells like coconut” or“smells like rubber” or “does not smell like gasoline” or “has apleasant smell” or “has an unpleasant smell” or the like.

The number of odorant descriptors is not necessarily the same as thenumber of odorant samples. It was found by the present inventors that arelatively small number of odorant descriptors is sufficient fordetermining the olfactory perception signature of the subject. Thenumber of odorant descriptors can therefore be smaller than the numberof odorant samples. For example, the number of odorant descriptors canbe from about 5 to about 15. The number of odorant descriptors isdenoted below by N.

While embodiments in which N<M are preferred, it is to be understoodthat embodiments in which N=M or N>M are also contemplated.

The rating controls that are displayed can be of any type generallyknown in the field of graphical user interface design. Representativeexamples include, without limitation, a slider, a dropdown menu, a combobox, a text box and the like. A representative set of human-languagedescriptors with a respective set of rating controls is illustrated inFIG. 1A.

Odorant descriptors sets that are presented at 12 for different odorantsamples need not to be disjoint sets. In preferred embodiments, anintersection set of at least two sets of odorant descriptors (one setfor each odorant sample) is a non-empty set, so that there is at leastone or at least two or more odorant descriptors (which are elements ofthe intersection between the sets) that is/are presented at 12 for twodifferent odorant samples. In some embodiments, the same set of odorantdescriptors is repeatedly presented for all odorant samples.

At 13, sniffing ratings are received from the subject. In embodiments inwhich rating controls are displayed, the user enters the ratings in therating controls, and the ratings are received from the rating controls.Each received rating is indicative of a descriptiveness of therespective odorant descriptor for the respective odorant sample, asperceived by the subject upon sniffing that odorant sample. For example,when the odorant descriptor is “has a pleasant smell,” the sniffingrating indicates to what extent the subject perceives the pleasantnessof the odor of the respective odorant.

Since the subject is presented with a set of N odorant descriptors foreach odorant sample, the method preferably obtains at 13 a set s ofperceived descriptiveness levels p(1,k), p(2,k), . . . , p(N,k) for eachodorant sample k. The descriptiveness levels are preferably numericalaccording to a predetermined scale, for example, 0 to 100. The ratings,on the other hand, are not necessarily numerical. For example, theratings can be positions on a slider or textual phrases from a dropdownmenu. In embodiments in which the ratings are not numerical, the methodoptionally and preferably parses the ratings and maps them to numericaldescriptiveness levels according to a predetermined mapping protocol. Itis appreciated, however, that some subjects may not provide a rating foreach and every odorant descriptor that is displayed, since, for example,some subjects may find a particular odorant descriptor irrelevant for aparticular odorant sample. In such a scenario, the method can excludethe particular descriptiveness level from the set of descriptivenesslevel that corresponds to the respective odorant sample, so that thesize of the set s is less than N for the respective odorant sample.Alternatively, the method can substitute a value for that particulardescriptiveness level, according to a predetermined procedure, so thatthe size of the set s remains N. The substituted value is preferably astatistical measure, such as, but not limited to, the mean or median ofall descriptiveness levels that were obtained from the subject for thesame odorant descriptor after sniffing other odorant samples.

Once all the ratings are received for all the odorant samples, acollection C, including M sets s₁, s₂, . . . , s_(M) of descriptivenesslevels, is obtained.

At 14, an olfactory perception signature of the subject is calculatedbased on the ratings. This is optionally and preferably executed bycalculating relations between pairs of sets of the collection C, whichpairs of sets correspond to pairs of odorant samples. This provides avector v of relations, which vector represents the olfactory perceptionsignature of the subject. The dimension of the vector v is thereforeequal to or less than M(M−1)/2 which is the numbers of pairs in thecollection C. Thus, denoting the relation between set s_(i) ofcollection C and set s_(j) of collection C by r_(ij), wherei,j≤M(M−1)/2, the components of the vector v are r₁₂, r₁₃, r₂₃, etc.When the vector v has its maximal dimension M(M−1)/2, namely when allpossible pairwise relations are calculated, the vector v can be writtenas v=(r₁₂, r₁₃, . . . , r_(1M), r₂₃, . . . , r_(M-1,M)).

The relations can be calculated in more than one way. In one embodiment,squared differences (p(k,i)−p(k,j))² between descriptiveness levelsp(k,i) of a first odorant sample i of the pair, and respectivedescriptiveness levels p(k,j) of a second odorant sample j of the pairare calculated. The squared differences can be averaged for each pair(i,j) of odorant samples, for example, by summing over the odorantdescriptor index k, and dividing by the size N of the sets s.Thereafter, a square root of this average is optionally and preferablyobtained to provide the relation value r_(ij) between odorant sample iand odorant sample j (or, equivalently between set s_(i) and set s_(j)).These embodiments are schematically illustrated in FIG. 1A.

It is appreciated that such a calculation of the relation r_(ij) isequivalent to a normalized Euclidian distance between two vectorsu_(i)=(p(1,i), p(2,i), . . . , p(N,i)) and u_(j)=(p(1j), p(2,j), . . . ,p(N j)) each vector being formed of one set of descriptiveness levelsand therefore represent one odorant sample, wherein the normalizationfactor is the square root of the dimension of the vectors u_(i) andu_(j) (the number of descriptiveness levels in each set).

In another embodiment, the relation r_(ij) is calculated using anon-Euclidian distance between the two vectors u_(i) and u_(j).Optionally, the non-Euclidian distance can be normalized by the squareroot of the dimension of the vectors. Representative examples ofnon-Euclidian distance include, without limitation, a Chebyshevdistance, a Manhattan distance, and a Minkowski distance. Otherrelations between pairs of sets, such as a statistical correlation(e.g., Pearson correlation, Spearman correlation, Kendall correlation)or a t-test distance between the vectors u_(i) and u_(j), are alsocontemplated.

In cases in which a relation r_(ij) is calculated for vectors ofdifferent size the calculation optionally and preferably includes onlydescriptiveness levels that correspond to odorant descriptors to whichthe subject provided ratings for both odorant samples i and j.

The relations r_(ij) provide indication regarding the similarity betweenthe respective sets. It is appreciated that whether the calculatedrelation r_(ij) increases or decreases with the level of similaritybetween the sets depends on the procedure employed for calculating therelations r_(ij). For example, when the calculation is based ondistances (Euclidian distance, non-Euclidian distance, t-test distance),high value of the calculated relations r_(ij) indicates low similaritylevel, and when the calculation is based on correlation, high value ofthe calculated relations r_(ij) indicates high similarity level.

The method can optionally and preferably proceed to 15 at which agraphical output describing the vector of similarities is generated. Thegraphical output can be a color coded output. A representative exampleof such an output is shown in FIGS. 2A-2E, described in greater detailin the Examples section that follows.

The method can optionally and preferably proceed to 16 at which anolfactory perception signature of another subject is obtained and to 17at which the olfactory perception signatures are compared. Thecomparison can be based, for example, on a metric selected from thegroup consisting of statistical correlation (e.g., Pearson correlation,Spearman correlation, Kendall correlation), Euclidian distance,Log-Euclidean distance, Angular distance, significance test (e.g.,t-test) distance, Chebyshev distance, Manhattan distance, Minkowskidistance and the like. Thus, for example, a distance or statisticalcorrelation between the olfactory perception signatures can becalculated and the calculated value of the distance or statisticalcorrelation can be used as a similarity measure describing the level ofsimilarity between the two signatures. For example, when a statisticalcorrelation is calculated, higher correlation value indicates highersimilarity between the signatures, and when an Euclidian distance, aLog-Euclidean distance, a non-Euclidean distance, an Angular distance ora significance test distance is calculated, lower distance valueindicates higher similarity between the signatures.

The comparison can be utilized in more than one way. In someembodiments, the comparison is utilized for matching between members ofa community, e.g., an online community. For example, based on thecomparison, a likelihood for successful relationship between the subjectand the other subject can be determined, wherein when the signatures aremore similar the likelihood for successful relationship is higher andwhen the signatures are less similar the likelihood for successfulrelationship is lower.

In some embodiments, the comparison is utilized for determininglikelihood for HLA matching between the subject and other subject,wherein when the signatures are more similar the likelihood for HLAmatching is higher and when the signatures are less similar thelikelihood for HLA matching is lower. As demonstrated in the Examplessection that follows, it was found by the present Inventors thatsimilarity between the olfactory fingerprints of the present embodimentsis significantly higher for highly HLA-matched individuals than forpoorly HLA-matched individuals.

The olfactory perception signature of the other subject can becalculated as described above. Alternatively, the olfactory perceptionsignature to which the subject's signature is compared can be obtainedfrom an entry in a database of olfactory perception signatures.

Each entry in such a database can include olfactory perception signatureand annotation information. The annotation information can be storedseparately from the olfactory perception signature (e.g., in a separatefile on a computer readable medium). The annotation informationcorresponds to the individual or individuals for which the databaseolfactory perception signature pertains. For example, the annotationinformation can include details of the community member that ischaracterized by the database olfactory perception signature. Theannotation information can alternatively or additionally include HLAdata of the individual that is characterized by the database olfactoryperception signature.

Also contemplated are embodiments in which the annotation informationrelates to psychological traits, for example, each olfactory perceptionsignature can be associated with a psychological trait (e.g., opennessto experience, conscientiousness, extraversion, agreeableness,neuroticism), and a high similarity between the signature of the subjectand the database signature can be indicative that the subject can bedescribed by the respective psychological trait. Thus, a search within adatabase of psychologically annotated olfactory perception signaturesallows determining the psychological condition of the subject, and/orpredicting an outcome of a psychological test for the subject.

Representative examples of psychological tests for which the results canbe predicted by the present embodiments include, without limitation,NEO-PI, 16PF, Occupational Personality Questionnaire, Beck DepressionInventory, Glover Numbing Scale, Eysenck Personality Questionnaire, LifeExperiences Survey, Perceived Stress Scale, State-Trait AnxietyInventory (STAI) Form Y-2, STAI Form Y-1, Pittsburgh Sleep QualityIndex, Kohn Reactivity Scale, Pennebaker Inventory for LimbicLanguidness, Short Form 12 Health Survey v2, SF-36, Pain CatastrophizingScale, In vivo Coping Questionnaire, Coping StrategiesQuestionnaire-Rev, Lifetime Stressor List& Post-Traumatic StressDisorder (PTSTD) Checklist for Civilians, Multidimensional PainInventory v3, Comprehensive Pain & Symptom Questionnaire, SymptomChecklist-90-R (SCL-90R), Brief Symptom Inventory (BSI), Beck DepressionInventory (BDI)1 Profile of Mood States Bi-polar, Pain IntensityMeasures, and Pain Unpleasantness Measures.

The method ends at 18.

The determination of olfactory perception signature and the optionalcomparison to another olfactory perception signature can be executedaccording to some embodiments of the present invention by aserver-client configuration, as will now be explained with reference toFIG. 13.

FIG. 13 illustrates a client computer 30 having a hardware processor 32,which typically comprises an input/output (I/O) circuit 34, a hardwarecentral processing unit (CPU) 36 (e.g., a hardware microprocessor), anda hardware memory 38 which typically includes both volatile memory andnon-volatile memory. CPU 36 is in communication with I/O circuit 34 andmemory 38. Client computer 30 preferably comprises a graphical userinterface (GUI) 42 in communication with processor 32. I/O circuit 34preferably communicates information in appropriately structured form toand from GUI 42. Also shown is a server computer 50 which can similarlyinclude a hardware processor 52, an I/O circuit 54, a hardware CPU 56, ahardware memory 58. I/O circuits 34 and 54 of client 30 and server 50computers preferable operate as transceivers that communicateinformation with each other via a wired or wireless communication. Forexample, client 30 and server 50 computers can communicate via a network40, such as a local area network (LAN), a wide area network (WAN) or theInternet. Server computer 50 can be in some embodiments be a part of acloud computing resource of a cloud computing facility in communicationwith client computer 30 over the network 40.

GUI 42 and processor 32 can be integrated together within the samehousing or they can be separate units communicating with each other. GUI42 can optionally and preferably be part of a system including adedicated CPU and I/O circuits (not shown) to allow GUI 42 tocommunicate with processor 32. Processor 32 issues to GUI 42 graphicaland textual output generated by CPU 36. Processor 32 also receives fromGUI 42 signals pertaining to control commands generated by GUI 42 inresponse to user input. GUI 42 can be of any type known in the art, suchas, but not limited to, a keyboard and a display, a touch screen, andthe like. In preferred embodiments, GUI 42 is a GUI of a mobile devicesuch as a smartphone, a tablet, a smartwatch and the like. When GUI 42is a GUI of a mobile device, processor 32, the CPU circuit of the mobiledevice can serve as processor 32 and can execute the code instructionsdescribed herein.

Client 30 and server 50 computers can further comprise one or morecomputer-readable storage media 44, 64, respectively. Media 44 and 64are preferably non-transitory storage media storing computer codeinstructions as further detailed herein, and processors 32 and 52execute these code instructions. The code instructions can be run byloading the respective code instructions into the respective executionmemories 38 and 58 of the respective processors 32 and 52. Storage media64 preferably also store one or more databases including a database ofpsychologically annotated olfactory perception signatures as furtherdetailed hereinabove.

In operation, processor 32 of client computer 30 displays on GUI 42 aset of rating controls, such as, but not limited to, a slider, adropdown menu, a combo box, a text box and the like. Preferably,processor 32 also displays on GUI 42 a set of odorant descriptors,respectively corresponding to the set of rating controls, as furtherdetailed hereinabove and exemplified in the upper left pane of FIG. 1A.A subject, which can be a member of an online community, and which isprovided with physical odorant samples (e.g., samples 122) for sniffing,enters the sniffing ratings using the rating controls displayed on GUI42.

Processor 32 receives the subject's ratings from GUI 42 and cancalculate an olfactory perception signature of the subject based onthese ratings. For example, similarities between pairs of sets ofdescriptiveness levels can be calculated to provide a vector ofsimilarities as further detailed hereinabove. Processor 32 can thentransmit the olfactory perception signature to server computer 50.

Alternatively, processor 32 can receive the subject's ratings from GUI42 and transmit these ratings to server computer 50. In theseembodiments, the calculation of the olfactory perception signature ofthe subject, based on the transmitted ratings, is executed by servercomputer 50, e.g., by calculating the similarities between pairs of setsto provide a vector of similarities as further detailed hereinabove.

Server computer 50 can access a database of olfactory perceptionsignatures stored on media 64, search searches the database for adatabase olfactory perception signature that is similar to the olfactoryperception signature of the subject, and, when such similar databasesignature is found, transmit to client computer 30 an indication that asimilar database olfactory perception signature has been found. Clientcomputer 30 can receive the indication from server computer 50 anddisplay it on GUI 42.

Server computer 50 can also transmit to client computer 30 theannotation information associated with the similar database signature,and client computer 30 can display this information on GUI 42. Forexample, when the comparison between signatures is for the purpose ofsocial matching, the server computer 50 can pull from the databasemember details pertaining to the member associated with the founddatabase signature, and transmit these details to client computer 30 fordisplaying on GUI 42.

As used herein the term “about” refers to ±10%.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Various embodiments and aspects of the present invention as delineatedhereinabove and as claimed in the claims section below find experimentalsupport in the following examples.

EXAMPLES

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments of the invention in a nonlimiting fashion.

Generally, the nomenclature used herein and the laboratory proceduresutilized in the present invention include molecular, biochemical,microbiological and recombinant DNA techniques. Such techniques arethoroughly explained in the literature. See, for example, “MolecularCloning: A laboratory Manual” Sambrook et al., (1989); “CurrentProtocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed.(1994); Ausubel et al., “Current Protocols in Molecular Biology”, JohnWiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide toMolecular Cloning”, John Wiley & Sons, New York (1988); Watson et al.,“Recombinant DNA”, Scientific American Books, New York; Birren et al.(eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, ColdSpring Harbor Laboratory Press, New York (1998); methodologies as setforth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis,J. E., ed. (1994); “Culture of Animal Cells—A Manual of Basic Technique”by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; “Current Protocolsin Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al.(eds), “Basic and Clinical Immunology” (8th Edition), Appleton & Lange,Norwalk, Conn. (1994); Mishell and Shiigi (eds), “Selected Methods inCellular Immunology”, W. H. Freeman and Co., New York (1980); availableimmunoassays are extensively described in the patent and scientificliterature, see, for example, U.S. Pat. Nos. 3,791,932; 3,839,153;3,850,752; 3,850,578; 3,853,987; 3,867,517; 3,879,262; 3,901,654;3,935,074; 3,984,533; 3,996,345; 4,034,074; 4,098,876; 4,879,219;5,011,771 and 5,281,521; “Oligonucleotide Synthesis” Gait, M. J., ed.(1984); “Nucleic Acid Hybridization” Hames, B. D., and Higgins S. J.,eds. (1985); “Transcription and Translation” Hames, B. D., and HigginsS. J., eds. (1984); “Animal Cell Culture” Freshney, R. I., ed. (1986);“Immobilized Cells and Enzymes” IRL Press, (1986); “A Practical Guide toMolecular Cloning” Perbal, B., (1984) and “Methods in Enzymology” Vol.1-317, Academic Press; “PCR Protocols: A Guide To Methods AndApplications”, Academic Press, San Diego, Calif. (1990); Marshak et al.,“Strategies for Protein Purification and Characterization—A LaboratoryCourse Manual” CSHL Press (1996); all of which are incorporated byreference as if fully set forth herein. Other general references areprovided throughout this document. The procedures therein are believedto be well known in the art and are provided for the convenience of thereader. All the information contained therein is incorporated herein byreference.

General Methods Subjects

A total of 238 generally healthy subjects participated in threeexperiments. Experiment 1A: 89 subjects, 40 women, mean age=25.7±3.1years; Experiment 1B: 18 subjects, 11 women, mean age 26.8±3.4;Experiment 2: 130 subjects, 65 women, mean age=29.93±8.44 years).

Odorants

Two forms of odorant presentation were used in this study. Experiment 1Acontained 24 odorants in scratch-and-sniff form provided by The PrintBoxInc (NY, USA) and 4 odorants presented in sniff-jars. Experiment 1Bcontained 22 odorants presented in sniff-jars. Experiment 2 contained 11odorants all in jars; the 4 jar odorants from Experiment 1(isoamyl-acetate, 1,8-cineole, cis-3-hexen-1-ol, isovaleric acid) and 7additional odorants. Because the initial test-retest experiment usedmostly scratch-and-sniff odorants and the second test-retest experimentused jars, any difference between these methods of presentation could beassessed. No significant difference in test-retest was found whencomparing scratch-and-sniff (r=0.59±0.14) to jars (r first-second=0.58,±0.21, t(39)=0.24, p=0.81).

List of Odorants Used for Experiment 1A

1. Moth ball; 2 Eucalyptus; 3 Strawberries; 4 Burnt rubber; 5 Sweat; 6Natural gas; 7 Dill pickle; 8 Fish; 9 Cigar; 10 Manure; 11 Musk; 12Ashtray; 13 Root beer; 14 Compost; 15 Green apple; 16 Cheese; 17 Mango;18 Garlic; 19 Maple; 20 Anise; 21 Rose; 22 Blue spruce; 23 Clove; 24Banana; 25 Banana (isoamyl acetate); 26 Eucalyptus (1,8-cineole); 27 Wetgrass (cis-3-hexen-1-ol); 28 Isovaleric acid.

Odorants 1-24 were presented as scratch-and-sniff cards (obtained fromThe Print Box, Inc.). Odorants 25-28 were single molecules presented insmall jars.

List of Descriptors Used for Experiment 1A

1. Fishy; 2 Sour milk; 3 Hot cool; 4 Coconut; 5 Aromatic; 6 Fresh eggs;7 Crushed grass; 8 Anise; 9 Burnt candle; 10 Household gas; 11 Almond;12 Fruity (not citrus); 13 Citrus; 14 Creosote; 15 Cologne; 16 Floral;17 Chocolate; 18 Oily/fatty; 19 Medicinal; 20 Woody/resinous; 21Pleasant; 22 Nutty; 23 Nail polish remover; 24 Strawberry; 25 Poisonousedible; 26 Mild/intense; 27 Annoying/soothing; 28 Pleasant; 29 Familiar;30 Weak/strong; 31 Sweet; 32 Clean/dirty; 33 Causes physicaltension/causes physical relaxation; 34 Feminine; 35 Fresh/stale; 36Dull/sharp; 37 Volatile; 38 Repulsive/attractive; 39 Nose stuffing/noseopening; 40 Artificial/natural; 41 Burnt; 42 Erotic; 43 Green; 44Medicinal; 45 Salty; 46 Hot/cold; 47 Bitter; 48 Sour; 49 Heavy/light; 50Smoked; 51 Poisonous/edible; 52 Masculine; 53 Disgusting.

List of Odorants Used for Experiment 1B

1. Ambrarome absolu; 2 Anisic aldehyde/aubepine; 3 Castoreum artessresin 246/2 IFR; 4 Carvone laevo; 5 Cistus labdanium oil Spain RB; 6Civet artessence absolute; 7 Eucaliptus globulus oil China; 8 Fennel oilsweet; 9 Fir balsam oil Canada; 10 Galbanum oil concentrated; 11 Gingerol; 12 Grapefruit oil California; 13 Guava duplcation CS; 14 Hexanol3-CIS; 15 Hydrocarboresin SB; 16 Jasmin absolute communelle; 17 Nutmegoil Indonesia; 18 Pepper black oil; 19 Peppermint oil; 20 Peru balsamoil; 21 Vitiver oil Haiti; 22 Mugest C5 RIFM.

List of Descriptors Used for Experiment 1B

1. Body odor; 2 Pleasant; 3 Fresh/rotten; 4 Sweet; 5. Poisonous/edible;6 smooth/textured; 7 Flowery; 8 Feminine; 9 Light/heavy; 10 Erotic; 11Cold/hot; 12 Weak/strong; 13 Burnt; 14 Sour; 15 Masculine; 16 Complex;17 Clean/dirty; 18 Artificial/natural; 19 Calming/disturbing; 20Dull/sharp; 21 Bitter; 22 Dry/wet; 23 Aromatic.

Ratings

Each subject rated 28 odorants along 54 verbal descriptors in Experiment1A, 22 odorants along 23 verbal descriptors in Experiment 1B, and 11odorants along 57 verbal descriptors in Experiment 2, using visualanalogue scales (VAS). For example, the question “please rate theodorant” was displayed together with a 14 cm line ranging from “not atall smells like coconut” at one end, to “very much smells like coconut”at the other end. After sniffing the odorant presented inscratch-and-sniff or jar, participants crossed the line at a pointreflecting their perception, and the line was later parsed to 100 foranalysis. Odor order was random across participants, andinter-odor-interval was >40 seconds. To account for individualdifferences in use of scales, each subject's data was normalized byfirst subtracting the minimal value applied by the subject, thendividing by the maximal remaining value, and multiplying by 100. Thisgenerated a normalized range between 0 and 100.

HLA Typing

5-10 mL of blood were drawn from each volunteer and kept at 4° C. untilDNA was extracted. Genomic DNA Extraction was carried out from 400 μL ofwhole blood using the MagNA Pure Compact Nucleic Acid Isolation Kit I(Roche Diagnostics GmbH, Mannheim, Germany). DNA samples were stored at−20° C. HLA typing was performed utilizing LUMINEX™ technology andImmucor Transplant Diagnostic (Stamford, Conn.) kits to obtain HLA A*,B*and DRB1* loci typings at low/intermediate resolution.

HLA Matching

HLA match was calculated using methods previously reported in reference(16). There are three general groups of HLA: HLA-A*, HLA-B*and HLADRB1*,and within each group there are different specific HLA proteins (thereare 59 different HLA-A* proteins, 118 different HLA-B*and 124 differentHLA-DRB1*). Each of these HLA groups (A*, B*and DRB1*) is notated by a2-digit numerical designation (e.g. HLA-A* 01:07 HLA-B*15:15,HLA-DRB1*15:33). A match is calculated by counting the number of HLAproteins (in each group separately) present in one subject that are alsopresent in another subject. Since there are 2 digits for each HLA group,the count can be 0, 1 or 2. Once the count for each HLA group isobtained, a match is calculated by summing the values of all the groups.In other words, for each donor/recipient pair, the number of antigenspresent in the donor that matched an antigen in the recipient werecounted. Homozygous antigens in the recipient that matched a donorantigen were counted as two matches. Since three HLA loci were counted,there was a potential for a maximum of 7 matches. For example if donor Ahas the following HLA genotype A*24,68 B*14,35 DRB1*01, 11 and recipientB has the following HLA genotype A*03,23 B*41, 47 DRB1*10,11 this pair(A->B) will have an HLA match score of 0+0+1=1. However if donor C hasthe following HLA genotype A*02, 30 B*13, 50 DRB1*07, 07 and recipient Dhas the following HLA genotype A*02, 02 B*27, 41 DRB1*07, 11 then thepair (C->D) will have an HLA match score of 2+0+1=3 and the pair (D->C)will have an HLA match score of 1+0+2=3 (note that even though the totalHLA match score is the same it is not symmetric between C<->D).

Distance Metrics

The present embodiments contemplate several types of metrics tocalculate the distance between olfactory fingerprints. In the presentExample, three different distance metrics were used to define thedistance between olfactory fingerprints of subject i and subject j (i.e.d_(i,j)). These include, (A) Correlation, (B) Euclidean distance, and(C) Log-Euclidean distance. These metrics were compared, and theirimpact on discriminability and stability were evaluated. In all metrics,the olfactory fingerprint FP was calculated in the same manner (see EQs.1 and 2 below). The notation FP_(i) ^(A) denotes fingerprint of subjecti, generated using set A of descriptors or during session A and FP_(j)^(B) denotes fingerprint of subject j, generated using set B ofdescriptors or during session B. To evaluate the effectiveness of eachmethod several indices were defined.

a) Z-value index: Comparison of within-subject (intrasubject) distanceto between-subjects (intersubject) distance by using differentdescriptors during the same session. This index can be determined foreach subject by calculating how many SDs his/hers intersubject's scorelies from the mean of the distribution of intrasubject scores (thedistribution of intrasubject scores is calculated only between onesubject and all others).

To calculate the Z-Value, a Gaussian was fitted to the distribution ofall intrasubject distances and calculated the mean and standarddeviation of the fitted Gaussian. Then the quantity

$Z_{j} = \left( \frac{x_{j} - {{mean}\left( x_{i} \right)}}{{sd}\left( x_{i} \right)} \right)_{i \neq j}$

was calculated for determining how far from the mean an individualtypically falls. Thus, for each row of the distances matrix, the valuein the diagonal of the matrix was compared to a Gaussian fitted to thedistribution of all non-diagonal values. The individual Z-scores wereaveraged and to obtain an average Z-value index.

b) PERMANOVA Pseudo-F value: Permutational Multivariate ANOVA(PERMANOVA) was used to compare intrasubject distance to intersubjectdistance. PERMANOVA implements a flexible non-parametric distance-basedanalogue of analysis of variance for multivariate data that provides adistribution-free means of testing differences between treatments intheir multivariate profile (Anderson, 2001).

c) Same as (a) but using same descriptors during the different sessions.

d) Same as (b) but using same descriptors during the different sessions.

e) P-value index: Using Wilcoxon rank sum test to compare olfactoryfingerprint distance of low and high HLA match.

1. Distance Metric A: Correlation

Pearson correlation was used as a metric for distances (d_(i,j)) betweenolfactory fingerprints, d_(i,j)=corrolation(FP_(i) ^(A),FP_(j) ^(B)),more specifically,

d_(i,j)=COV(FP_(i) ^(A),FP_(j) ^(B))/(σ(FP_(i) ^(A))σ(FP_(j) ^(B))),where COV denotes a covariance as and σ denotes a standard deviation ofthe respective olfactory fingerprint.

a. Different descriptors comparison, see (a) above: Mean differencebetween individual's two fingerprints d=0.76±0.02, mean differencebetween two different individual's fingerprints d=0.25±0.007. AverageZ-value Z=4.91. FIGS. 8A-B.

b. PERMANOVA test comparing intrasubject distance to intersubjectdistance within the same session but using different descriptors: pseudoF=8.16, p<10⁻⁶. FIG. 8C.

c. Different sessions comparison, see (b) above: Mean difference betweenan individual's two fingerprints: d=0.58±0.15, mean difference betweentwo different individual's fingerprints d=0.31±0.076, Average Z-valueZ=2.67.

d. PERMANOVA test comparing intrasubject distance to intersubjectdistance between two sessions: pseudo F=4.23, p<10⁻⁶. FIG. 8D.

e. Wilcoxon rank sum test comparing olfactory fingerprint distance oflow and high HLA match: Z=2.14, p<0.03. FIG. 8E.

2. Distance Metric B: Euclidean Distance

We used Euclidean distance as a metric for distances (d_(i,j)) betweenolfactory fingerprints:

d _(i,j)=√{square root over (Σ_(i=1) ^(n)(FP _(i) ^(A) −FP _(j)^(B))²)}  Formula:

Note: when using Euclidean distance as a metric for distances, thedistribution of olfactory fingerprints distances (d) is not Gaussian;hence a Z-score value might not be a good candidate for comparisonintrasubject to intersubject distance.

a. Different descriptors comparison, see (a) above: Mean differencebetween an individual's two fingerprints d=140±29, mean differencebetween two different individual's fingerprints d=280±34. AverageZ-value Z=2.82. FIGS. 9A-9B.

b. PERMANOVA test to compare intrasubject distance to intersubjectdistance within the same session but using different descriptors: pseudoF=7.3, p<10⁻⁶. FIG. 9C.

c. Different sessions comparison, see (b) above: Mean difference betweenan individual's two fingerprints: d=200±42, mean difference between twodifferent individual's fingerprints d=320±41, Average Z-value Z=1.75.

d. PERMANOVA test to compare intrasubject distance to intersubjectdistance between two sessions: pseudo F=4.23, p<10⁻⁶. FIG. 9D.

e. Wilcoxon rank sum test comparing olfactory fingerprint distance oflow and high HLA match: Z=3.21, p<0.0015. FIG. 9E.

3. Distance Metric C: Log-Euclidean Distance

To reshape the distribution of Euclidean distances (see 2 above) be moreGaussian log of Euclidean distance was used as a metric for distances(d_(i,j)) between olfactory fingerprints:

d _(i,j)=log(√{square root over (Σ_(i=1) ^(n)(FP _(i) ^(A) −FP ₁^(B))²)})  Formula:

a. Different descriptors comparison, see (a) above: Mean differencebetween an individual's two fingerprints d=5±0.22, mean differencebetween two different individual's fingerprints d=5.6±0.1. AverageZ-value Z=3.47. FIGS. 10A-10B.

b. PERMANOVA test to compare intrasubject distance to intersubjectdistance within the same session but using different descriptors: pseudoF=6.5, p<10⁻⁶. FIG. 10C.

c. Different sessions comparison, see (b) above: Mean difference betweenan individual's two fingerprints: d=5.3±0.2, mean difference between twodifferent individual's fingerprints d=5.7±0.13, Average Z-value Z=2.23.

d. PERMANOVA test to compare intrasubject distance to intersubjectdistance between two sessions: pseudo F=4.23, p<10⁻⁶. FIG. 10D.

e. Wilcoxon rank sum test comparing olfactory fingerprint distance oflow and high HLA match: Z=3.21, p<0.0015. FIG. 10E.

Derivation of Olfactory Fingerprints

Fingerprints were derived using a matrix of perceived odor similarities(21). A palette of 28 odors (listed above) that provided for 378pairwise similarities (28×27/2=378). Such a 378-dimensional olfactoryfingerprint allows for characterization of many individuals.

Rather than directly obtaining pairwise relation estimates, the presentinventors derived pairwise relation from 54 different descriptorsapplied to each odorant alone (listed above). A derived relation rating,as opposed to a direct relation rating, was selected because whereas thetwo are highly correlated, derived relation is much easier and faster toobtain. For example, direct relation ratings of the 378 possible odorantpairs in this study would entail 756 individual odorant presentations (Avs. B×378) each followed by one question: “rate similarity”. Such alarge number of odorant presentations (756) may be difficult to processby a human subject. On the other hand, 378 derived relation ratingsentail 28 odorant presentations each followed by several questions(e.g., “rate how coconut”, “rate how lemony”, etc), and derivation ofrelation from the answers. This remains a feasible experiment, andmoreover, the number of questions can later be reduced based on thecurrent analysis.

Use of odorant descriptors likely entails personal (23) and culturaldifferences (24), yet the technique optionally and preferably does notassume or rely on any agreement across individuals in the application ofa given descriptor (FIGS. 1A and 1B contain a schematic of fingerprintacquisition that explains this issue). Thus, an individual olfactoryfingerprint was calculated by computing all the pairwise distancesbetween all odorants rated. For a measure of distance between odorant kand odorant m, the following equation was used:

$\begin{matrix}{{distance}_{k,m} = \sqrt{\frac{\sum\limits_{i = 1}^{n}\; \left( {P_{i}^{k} - P_{i}^{m}} \right)^{2}}{n}}} & \left( {{EQ}.\mspace{14mu} 1} \right)\end{matrix}$

Where P_(i) ^(k) is the perceptual rating of odorant k using descriptori. and P_(i) ^(m) is the perceptual rating of odorant m using descriptori. In words: the distance between odorant k and odorant m is the squareroot of the mean of the squared difference between all perceptualratings for those two odorants. Once all the pairwise distances arecomputed, an olfactory fingerprint may be notated in the following form:

FP _(k+m−1)=distance  (EQ. 2)

Where k=1 . . . N and m=k+1 . . . N. In words; each element of theolfactory fingerprint is the distance between pairs of odorants, whereeach pairwise distance is calculate only once (sincedistance_(k,m)=distance_(m,k)) and the distance between an odorant toitself is omitted (since distance_(k,k)=0). Consequently, if N odors areuses to construct an olfactory fingerprint the resulting olfactoryfingerprint will have N×(N−1)/2 elements. To negate the impact ofvariance in the number of descriptors used by subjects (e.g., onesubject may have used only 50 of the 54 descriptors, and another only45, leaving all other descriptors unrated, see FIGS. 6A-6B and 7A-7B fordescriptor usage), each distance between odor-pairs was normalized bythe number of mutual descriptors used for this pair. For example, ifodors A and B were rated along 50 descriptors by subject 1 and 45descriptors by subject 2, the distance between odor pairs was dividingby √50 for subject 1 and by √45 for subject 2, thereby allowing directcomparison of the perceived distances between odors A and B for subject1 and 2.

The derivations of relation according to some embodiments of the presentinvention do not assume that people agree with each other along anygiven descriptor (e.g., “how coconut”), they only assume that a personagrees with him/herself (FIGS. 1A and 1B). In other words, such relationmatrices are odor dependent, but descriptor-independent. This wasverified in experiments where fingerprints for the same individualobtained with the same odorants but different descriptors remainedhighly correlated.

For fingerprint visualization purposes, a rectangular image wasgenerated by interpolating the 378 fingerprint values to 500 values.Then, these values were projected onto a 25×20 matrix, and atwo-dimensional interpolation was conducted, providing a 2K×2K matrix,which were then projected onto a semi-circle (FIGS. 2A-2D). Thisvisualization technique was applied for 89 subjects (40 women, meanage=25.7±3.1 years).

Olfactory Fingerprints were Individually Specific

To test whether olfactory perception is similar across individuals, thepresent inventors calculated the mean rating along each descriptor foreach odorant, and then calculated the correlation of each individualwith the mean. Note, here fingerprints are not being compared, butrather ratings along specific descriptors. This revealed thatindividuals are indeed similar to each other in their gross perception.For example, in all top 10 correlated descriptors the mean perceptionwas a very good predictor of individual perception (all r >0.68) (FIG.2E). Moreover, the primary perceptual dimension of odor pleasantness (2,26) was particularly highly correlated across individuals and odors, atr=0.73±0.1 (FIG. 2E). In other words, the average description of anodorant using common descriptors is a pretty good estimation of what anygiven individual will say about that odor.

The present inventors next set out to determine whether despite thisgross agreement on odor perception, the sensitive perceptual test of thepresent embodiments can uncover a unique olfactory perceptualfingerprint. Consistent with this hypothesis, it was found that acrossthe 89 subjects that were tested, no two subjects had the samefingerprint (e.g., FIG. 2A vs. FIG. 2D, FIG. 2F and FIG. 2G). Thepresent inventors calculated all pairwise distances between olfactoryfingerprints (using Pearson's correlation) and found that the averagecorrelation across individuals (omitting self-self) was r=0.3±0.13 (FIG.2G). In other words, whereas gross perception was similar acrossindividuals, a fine measure of perception revealed individual perceptionfor each of the 89 subjects that were tested.

Olfactory Fingerprints were Independent of Descriptor Identity

The emergence of 89 individual fingerprints alone does not necessarilyimply that individual olfactory perception was captured because one canobtain such a result (89 different fingerprints) with random odorrelation ratings. To verify that olfactory fingerprints capturedindividual olfactory perception, for each of the 89 Participants, thepresent inventors now generated two alternative fingerprints, A and B,each utilizing a random independent half of the descriptors used toderive relation. They computed a matrix of all the pairwise distancesbetween olfactory fingerprints A and B (89×89) and tested whether thedistance of a subject from him/herself (using different descriptors) wassmaller than the distance of a subject to anyone else. In other wordswhether, despite the use of different descriptors each time, a subjectremained more similar to him/herself than to anyone else (distancebetween fingerprints was estimated by correlation, see General methods,herein above). This was repeated 1000 times, each time selecting adifferent set of nonoverlapping descriptors for fingerprints A and B,and the distances between fingerprints was assessed.

The present inventors again plotted the heat-map correlation matrix ofeach individual with all other individuals, this time however eachpairwise distance is computed as the correlation between olfactoryfingerprints A and B (FIG. 3A). It was found that the distance betweenan individual's two fingerprints based on the same odors but differentdescriptors (the diagonal in FIG. 3A, and FIG. 2A vs. FIG. 2B) wasoverwhelmingly smaller than the average distance between two differentindividuals (non-diagonal values in FIG. 3A and FIG. 2A vs. FIG. 2D)(mean difference between an individual's two fingerprints r=0.75±0.025,mean difference between two different individual's fingerprintsr=0.25±0.008, paired t-test, t (999)=885.7, p<10⁻¹⁰) (FIG. 3B). It wasalso found that the maximum of the heat-map correlation matrix lies onthe diagonal (i.e. self-self correlation). In other words, olfactoryfingerprints A and B of the same individual were always more similarthan olfactory fingerprints of different individuals. The analysis ofthis data was repeated using a Permutational Multivariate ANOVA(PERMANOVA) to compare the distance between an individual's twofingerprints based on the same odors but different descriptors (e.g.,FIG. 2A vs. FIG. 2B) to the distance between two different individuals(e.g., FIG. 2A vs. FIG. 2D). PERMANOVA implements a flexiblenon-parametric distance-based analogue of analysis of variance formultivariate data that provides a distribution-free means of testingdifferences between treatments in their multivariate profile (27).Again, the mean difference between an individual's two fingerprints wasr=0.75±0.025, while the mean difference between two differentindividual's fingerprint was r=0.25±0.008 (PERMNOV test, pseudo F=8.16,p<10⁻⁶) (FIG. 8C). Thus, the fingerprint genuinely captured personalidentity, and a subject's odorant-specific olfactory fingerprint remainsunique even when different descriptors are used to construct it. Oncethe present inventors established the main effect using PERMANOVA, theyset out to extrapolate the ability of the olfactory fingerprint toidentify an individual beyond their sample. For this one needs tocalculate whether a subject's correlation to him/herself (calculatedbetween fingerprints A and B) is within the distribution of correlationsof a subject to all other subjects (between fingerprint A of a subjectto fingerprint A of all other subjects). In other words: in order toconclude that a subject has a unique fingerprint the intersubjectcorrelation should not belong (low probability) to the distribution ofintrasubject correlations. They fitted a Gaussian to the intrasubjectdistances distribution, then calculated how many SDs a intersubject'sscore lies from the mean of the distribution of intrasubject scores (i.eZ-Value). From this they determined the probability of a subject'scorrelations to him/herself to be within the distribution ofcorrelations of a subject to all other subjects (i.e. p-value). All theindividual Z-Value scores were averaged, and the overall p-value wascalculated. The distribution of correlations of a subject to all othersubjects is not Gaussian, and the subject's correlation to him/herselfis limited by 1 (or −1) hence other metrics for distance betweensubjects may yield modestly different results (see General Materials andmethods). They repeated this procedure 1000 times, each time randomlyhalving the descriptors used to derive relation (with one half used togenerate fingerprint A and the other half used to generate fingerprintB), and averaged across all iterations, and all subjects. They obtainedan average Z-Value of 4.9 that corresponds to an ability to use the28-odor olfactory fingerprint to identify one person out of about twomillion individuals.

Fingerprint Specificity

In initial experiments, as many as 54 descriptors and 28 odors were usedbecause the present inventors wanted to explore the impact of theseparameters. To estimate the dependence of fingerprint discriminabilityon the number of descriptors and odorants used, the present inventorsagain generated two alternative fingerprints for each subject, A and B,each utilizing a random independent half of the descriptors used toderive relation. Here, however, the number of odorants and descriptorsused was successively reduced. Each analysis was repeated 1000 times,each time shuffling the particular odorants and descriptors omitted. Theaveraged fingerprint specificity (averaging the Z-Score across subjectsand iterations) was plotted as a function of the number of descriptorsand odorants used to generate it (FIGS. 4A, 4B). A monotonic decrease inthe specificity of the fingerprint was observed, yet even with only 7odors and 11 descriptors the correlation between an individual's twofingerprints based on the same odors but different descriptors wassignificantly above the correlation between two different individualsz=1.65, p<0.05. Thus, meaningful olfactory fingerprints can be obtainedin under 10 minutes. In turn, the present inventors extrapolated toestimate how many odors and descriptors were necessary in order toobtain an individual fingerprint for each of the ˜7 billion people onearth, and reached at 34 odors and 35 descriptors. Obtaining such adetailed fingerprint would take approximately 5 hours.

Olfactory Fingerprints Remained Specific Despite Fluctuation Over Time

Olfactory perception is not only variable across individuals; it is alsohighly variable within individuals over time (28). This variability mayreflect in part that odor perception is the combination of a givenreceptor activation pattern with the fluctuating homeostatic state inwhich it is perceived (hunger/satiety, mood, arousal, etc.) (2). To testthe persistence of the olfactory fingerprint at retest, 23 participantswere refingerprinted at a time ranging between 10 and 30 days followingtheir initial fingerprinting (e.g., FIG. 2A vs. FIG. 2C). It was foundthat the average distance of a person from him/herself remainedsignificantly lower than the average distance between differentindividuals (mean difference between an individual's two fingerprintsover time r=0.58±0.15, mean difference between two differentindividual's fingerprints r=0.31±0.076, paired t test, t (44)=7.69,p<10-8) (FIG. 4C). In other words, despite the passage of time, a personremained significantly more correlated with him/her self than withothers.

Despite the above result, the slight reduction in self-self correlationover retests raises the concern that given additional retests theself-self correlation advantage may disappear altogether. To addressthis concern the present inventors re-fingerprinted an additional groupof 18 subjects across five fingerprinting sessions that spanned 14 to 30days (list of odorants and descriptors for experiment 1B provided in thematerials and method section). A repeated measures ANOVA revealed thatat each repetition (II, III, IV, V) the average distance of a personfrom his/her first fingerprint remained unchanged (F(17,3)=2.24, p=0.09,mean difference between an individual's two fingerprints across retestsr first-second=0.58±0.21, r first-third=0.54±0.18, rfirst-fourth=0.54±0.2, r first-fifth=0.49±0.19) (FIG. 4D yellow).Moreover, the fingerprint stability in fact improved after the firstretest (F(17,3)=6.08, p<0.001) such that the second to third(r=0.66±0.19), third to fourth (r=0.68±0.20), and fourth to fifth(r=0.69±0.16) repetitions were all significantly better than the firstto second (r=0.58±0.21, all t(17)>2.67, all p<0.02) (FIG. 4D). Takentogether it may be concluded that despite the passage of time andrepeated testing, a person remained significantly more correlated withhim/her self than with others. The present inventors recalculated theability of the olfactory fingerprint to identify an individual beyondthe sample, this time comparing the initial and the later (few weekslater) fingerprints with the all the other subjects, and observed adecreased yet significant discriminability (z1−2=2.67, p<0.01,z1-3=2.67, p<0.01, z1−4=2.67, p<0.01, z1−5=2.67, p<0.01) (FIG. 4D blackand red). This amounts to an ability to use the current olfactoryfingerprint to identify one subject out of about three hundredindividuals. Moreover, given this variability over time, to effectivelyobtain long-lasting olfactory fingerprints for the entire worldpopulation it was found by extrapolation that rather than 34 odors with35 descriptors 160 odors are needed with 35 descriptors. Note that thisreduced discriminability is not only because of the extent of shift infingerprint over time; self correlation over time decreased from r=0.75to r=0.58, which remains significantly higher than the correlationacross individuals. However, because on average all subjects shifted inthis way, the ability to identify one person out of a crowd issignificantly reduced.

Similar Olfactory Fingerprints Imply High HLA Matching

The hypothesis underlying the present effort was that fingerprints wouldprovide a unique perceptual counterpart of an individual's uniqueolfactory receptor subtype genome. Consistent with this notion, 28-odorbased fingerprints were special to the tune of 1-in-two million. Thepresent inventors set out to test whether olfactory fingerprints cannevertheless remain informative of genetic traits linked to olfaction,in this case HLA. To test this the present inventors studied anadditional 130 subjects (65 women, mean age=29.93±8.44 years) whoprovided blood samples for HLA typing (see methods), and olfactoryfingerprints using the following 11 odors:

1. Isoamyl acetate

2 Vanillin

3 Isovaleric acid

4 cis-3-hexen-1-ol, wet grass

5 Androstadienone

6 Dibutyl amine

7 Ethyl pyrazine: 2-ethyl pyrazine

8 Eucalyptol (1,8-cineole)

9 Hexanol: 1-hexanol

10 Methyl anthranilate

11 Tolualdehyde: Ortho-tolualdehyde

Combinatorically, 130 subjects provide for 16770 possibledonor-recipient pairs. This is because HLA match is not symmetric, i.e.,in a given pair, a subject can have a high HLA match as a donor but poorHLA match as a recipient (note that the terms donor and recipient areused to describe the directionality of HLA matching). Therefore, 130subjects resulted in 16770 possible pairs (130*129) and not in 8385(130*129/2). For each pair, an olfactory fingerprint match wascalculated using Euclidean distance (see materials and methods) and anHLA match along a seven point scale (0-6, 0=no match) previouslydescribed (16). Only 65 out of 16770 possible pairs of individuals had ahigh HLA match of 5 or 6 (FIG. 5A).

It was found that the olfactory fingerprint match of these individualswas significantly better than the olfactory fingerprint match for poorlyHLA-matched individuals (HLA 5-6: mean olfactory fingerprint match inarbitrary units (AU) of Euclidean distance=12.7±4.1 [A.U.], HLA 1-4:mean olfactory fingerprint match=14.6±5.3 [A.U.], Wilcoxon rank sumtest: Z=3.2, p<0.0015). In other words, the olfactory perceptualfingerprint similarity was significantly informative on HLA matching,implying that it captured meaningful genetic information.

To further assess the strength of the link between olfactory fingerprintmatch and HLA match, the present inventors asked what would happen ifone used olfactory fingerprints to screen for potentially high HLAmatches in the population. To this end, the percentage of high HLAmatches one would potentially miss (incurred cost) versus the percentageof matches one could identify (gain) was calculated and presented usinga receiver operating characteristic (ROC) curve (red line—FIG. 5B).

It was found that all points in the ROC curve fall above theidentity-line; hence the olfactory fingerprints of the presentembodiments can identify pairs of individuals likely to have a high HLAmatch. Given the extended time needed to test 11 odorants as carried outin data collection, the present inventors next asked if they couldoptimize this test. To this end, the data were halved into training(8387 subject pairs) and testing sets (8385 subject pairs), eachmaintaining the original fractions of each level of HLA match. In thetraining set, the olfactory fingerprints was calculated using allpossible combinations of 3 to 11 odorants, and the 4 best-performingodorants were selected and then tested in the testing set. This wasrepeated 200 times (gray lines—FIG. 5B). Taking the median score (blackline—FIG. 5B), it was found that a selection of 4 odors (Isoamylacetate, Isovaleric acid, 2-Ethyl Pyrazine, and 1-Hexanol) decreased theaverage olfactory fingerprint distances of high HLA matched individualsto 10.8±3.7 [A.U.] compared to an olfactory fingerprint distance forpoorly HLA matched individuals of 13.8±5.8 [A.U.] (Z=4.35, p<0.000015).

The actual savings implicated were calculated as follows. The 65 highHLA matches in the data comprised 45 individuals (some individuals werematched with more than one). One individual was iteratively selectedfrom these 45 as “recipient”, and “donors” were randomly drawn until ahigh HLA match was encountered. This was repeated 10000 times.Consistent with the expectation from chance, an average of 65.35±37.5donors had to be tested in order to identify a match. These procedureswere then repeated, but rather than randomly drawing donors, they weredrawn in rank order in accordance with their rapidly obtainableoptimized olfactory fingerprint distance, starting with the closest. Itwas found that the average number of individuals that had to be testedin order to identify a match was 44±29, implying a 32% savings (t(64)=5.5, p<10⁻⁶). In other words, using this brief perceptual test onecould rank-order the population in order to save more than 30% of HLAtests.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

REFERENCES

-   1. Bushdid C, Magnasco M O, Vosshall L B, & Keller A (2014) Humans    candiscriminate more than 1 trillion olfactory stimuli. Science    343(6177):1370-1372.-   2. Yeshurun Y & Sobel N (2010) An Odor is Not Worth a Thousand    Words: From Multidimensional Odors to Unidimensional Odor Objects.    Annual Review of Psychology 61:219-241.-   3. Gilad Y & Lancet D (2003) Population differences in the human    functional olfactory repertoire. Mol Biol Evol 20(3):307-314.-   4. Keller A, Zhuang H, Chi Q, Vosshall L B, & Matsunami H (2007)    Genetic variation in a human odorant receptor alters odour    perception. Nature 449(7161):468-472.-   5. Araneda R C, Kini A D, & Firestein S (2000) The molecular    receptive range of an odorant receptor. Nat Neurosci    3(12):1248-1255.-   6. Axel R (1995) The molecular logic of smell. Sci Am    273(4):154-159.-   7. Buck L B (1996) Information coding in the vertebrate olfactory    system. Annu Rev Neurosci 19:517-544.-   8. Mainland J D, et al. (2013) The missense of smell: functional    variability in the human odorant receptor repertoire. Nat Neurosci    17(1):114-120.-   9. Menashe I, Man O, Lancet D, & Gilad Y (2003) Different noses for    different people. Nat Genet 34(2):143-144.-   10. Wandell B A (1995) Foundations of vision (Sinauer Associates).-   11. Wysocki C J & Beauchamp G K (1984) Ability to smell androstenone    is genetically determined. Proc Natl Acad Sci USA 81(15):4899-4902.-   12. Menashe I, et al. (2007) Genetic elucidation of human hyperosmia    to isovaleric acid. PLoS Biol 5(11):e284.-   13. Amadou C, et al. (2003) Co-duplication of olfactory receptor and    MHC class Igenes in the mouse major histocompatibility complex.    Human molecular genetics 12(22):3025-3040.-   14. Ziegler A, Dohr G, & Uchanska-Ziegler B (2002) Possible roles    for products of polymorphic MHC and linked olfactory receptor genes    during selection processes in reproduction. Am J Reprod Immunol    48(1):34-42.-   15. Younger R M, et al. (2001) Characterization of clustered    MHC-linked olfactory receptor genes in human and mouse. Genome    research 11(4):519-530.-   16. Jacob S, McClintock M K, Zelano B, & Ober C (2002) Paternally    inherited HLA alleles are associated with women's choice of male    odor. Nat Genet 30(2):175-179.-   17. Wedekind C, Seebeck T, Bettens F, & Paepke A J (1995)    MHC-dependent mate preferences in humans. Proc Biol Sci    260(1359):245-249.-   18. Wedekind C & Penn D (2000) MHC genes, body odours, and odour    preferences. Nephrology, dialysis, transplantation: official    publication of the European Dialysis and Transplant    Association-European Renal Association 15(9):1269-1271.-   19. Milinski M & Wedekind C (2001) Evidence for MHC-correlated    perfume preferences in humans. Behav Ecol 12(2):140-149.-   20. Christakis N A & Fowler J H (2014) Friendship and natural    selection. Proc Natl Acad Sci USA 111 Suppl 3:10796-10801.-   21. Wise P, Olsson M, & Cain W (2000) Quantification of odor    quality. Chem Senses 25(4):429-443.22. Khan R M, et al. (2007)    Predicting odor pleasantness from odorant structure: pleasantness as    a reflection of the physical world. J Neurosci 27(37):10015-10023.-   23. Keller A, Hempstead M, Gomez I A, Gilbert A N, & Vosshall L    B (2012) An olfactory demography of a diverse metropolitan    population. BMC Neurosci 13:122.-   24. Majid A & Burenhult N (2014) Odors are expressible in language,    as long as you speak the right language. Cognition 130(2):266-270.-   25. Dravnieks A (1982) Odor quality: semantically generated    multi-dimensional profiles are stable. Science 218:799-801.-   26. Schiffman S S (1974) Physicochemical correlates of olfactory    quality. Science 185(146):112-117.-   27. Anderson M J (2001) A new method for non!parametric multivariate    analysis of variance. Austral ecology 26(1):32-46.-   28. Stevens J C, Cain W S, & Burke R J (1988) Variability of    Olfactory Thresholds. Chemical Senses 13(4):643-653.-   29. Santoro S W & Dulac C (2012) The activity-dependent histone    variant H2BE modulates the life span of olfactory neurons. Elife    1:e00070.-   30. Cadiou H, et al. (2014) Postnatal Odorant Exposure Induces    Peripheral Olfactory Plasticity at the Cellular Level. The Journal    of Neuroscience 34(14):4857-4870.-   31. Wang H W, Wysocki C J, & Gold G H (1993) Induction of olfactory    receptor sensitivity in mice. Science 260(5110):998-1000.-   32. Yee K K & Wysocki C J (2001) Odorant exposure increases    olfactory sensitivity: olfactory epithelium is implicated. Physiol    Behav 72(5):705-711.-   33. Brown R E, Singh P B, & Roser B (1987) The major    histocompatibility complex and the chemosensory recognition of    individuality in rats. Physiol Behav 40(1):65-73.-   34. Freeman-Gallant C R, Meguerdichian M, Wheelwright N T, &    Sollecito S V (2003) Social pairing and female mating fidelity    predicted by restriction fragment length polymorphism similarity at    the major histocompatibility complex in a songbird. Molecular    ecology 12(11):3077-3083.-   35. Reusch T B, Haberli M A, Aeschlimann P B, & Milinski M (2001)    Female sticklebacks count alleles in a strategy of sexual selection    explaining MHC polymorphism. Nature 414(6861):300-302.-   36. Yamazaki K, et al. (1976) Control of mating preferences in mice    by genes in the major histocompatibility complex. The Journal of    experimental medicine 144(5):1324-1335.-   37. McRae J F, et al. (2012) Genetic variation in the odorant    receptor OR2J3 is associated with the ability to detect the “grassy”    smelling odor, cis-3-hexen-1-ol. Chem Senses 37(7):585-593.-   38. Kang N & Koo J (2012) Olfactory receptors in non-chemosensory    tissues. BMB Rep 45(11):612-622.-   39. Feldmesser E, et al. (2006) Widespread ectopic expression of    olfactory receptor genes. BMC Genomics 7:121.-   40. Busse D, et al. (2014) A Synthetic Sandalwood Odorant Induces    Wound Healing Processes in Human Keratinocytes via the Olfactory    Receptor OR2AT4. Journal of Investigative Dermatology.-   41. Pinto J M, Wroblewski K E, Kern D W, Schumm L P, & McClintock M    K (2014) Olfactory dysfunction predicts 5-year mortality in older    adults. PLoS One 9(10):e107541.-   42. Frasnelli J & Hummel T (2005) Olfactory dysfunction and daily    life. Eur Arch Otorhinolaryngol 262(3):231-235.-   43. Doty R L, et al. (1991) Olfactory dysfunction in three    neurodegenerative diseases. Geriatrics 46 Suppl 1:47-51.-   44. Perricone C, et al. (2013) Smell and autoimmunity: a    comprehensive review. Clin Rev Allergy Immunol 45(1):87-96.

What is claimed is:
 1. A client system for communicating in a matchingservice for matching members of an online community, the client systemcomprising: a transceiver arranged to receive and transmit informationon a communication network; and a processor arranged to communicate withthe transceiver, and perform code instructions, comprising: codeinstructions for displaying a set of rating controls on a userinterface; code instructions for receiving sniffing ratings entered by amember using said rating controls; code instructions for calculating anolfactory perception signature of the member based on said ratings; codeinstructions for transmitting said olfactory perception signature to aserver computer; and code instructions for receiving from said servercomputer an indication whether or not a matching member has been foundin a database, based on said transmitted olfactory perception signature.2. The system of claim 1, wherein said processor is arranged to displayon said user interface a set of odorant descriptors, respectivelycorresponding to said set of rating controls, wherein said sniffingratings are descriptiveness levels corresponding to said odorantdescriptors.
 3. The system of claim 2, wherein said processor isarranged to display said set of odorant descriptors and said a set ofrating controls a plurality of times, and to receive said sniffingratings a respective plurality of times, thereby to obtain a pluralityof sets of descriptiveness levels, wherein said calculating saidolfactory perception signature comprises calculating relations betweenpairs of sets of descriptiveness levels.
 4. The system of claim 3,wherein said calculation of said relations comprises, for each pair ofsets, averaging squared differences between descriptiveness levels of afirst set pair, and respective descriptiveness levels of a second set ofsaid pair.
 5. The system of claim 4, wherein said code instructionscomprise code instructions for determining and displaying likelihood forsuccessful relationship between the member and the matching member.
 6. Aserver system for communicating in a matching service for matchingmembers of an online community, the server system comprising: atransceiver arranged to receive and transmit information on acommunication network; and a processor arranged to communicate with thetransceiver, and perform code instructions, comprising: codeinstructions for receiving from a client computer an olfactoryperception signature of a member; code instructions for accessing acomputer readable database having a plurality of database olfactoryperception signatures of other members of the community; codeinstructions for searching said database for a database olfactoryperception signature that is similar to said olfactory perceptionsignature of the member; and code instructions for transmitting to saidclient computer an indication that a similar database olfactoryperception signature has been found.
 7. The system of claim 6, whereinsaid code instructions for searching said database employ comparison bya metric selected from the group consisting of statistical correlation,Euclidian distance, Log-Euclidean distance, Angular distance,significance test distance, Chebyshev distance, Manhattan distance, andMinkowski distance.
 8. A method of determining personality trait of asubject, the method comprising: providing the subject with a pluralityof physical odorant samples for sniffing; for each sniffed odorantsample, presenting to the subject, by a user interface, a set of odorantdescriptors and a respective set of rating controls, and receivingratings entered by the subject using said rating controls, each ratingbeing indicative of a descriptiveness of a respective odorant descriptorfor said odorant sample, thereby obtaining a set of descriptivenesslevels for said odorant sample; and calculating, by a computer,relations between pairs of sets of descriptiveness levels correspondingto pairs of odorant samples, to provide a vector of relations, saidvector representing the olfactory perception signature of the subject;accessing a computer readable database, each entry of said databasehaving a database olfactory perception signature and annotationinformation pertaining to a personality trait; searching said databasefor a database olfactory perception signature that is similar to saidolfactory perception signature of the subject; extracting from thedatabase annotation information associated with said similar databaseolfactory perception signature; and generating an output indicative of arespective personality trait.
 9. The method of claim 8, comprisingdetermining a psychological condition of the subject based on saidextracted annotation information.
 10. The method of claim 8, whereineach of at least some annotation information of said database isopenness to experience.
 11. The method of claim 8, wherein each of atleast some annotation information of said database is conscientiousness.12. The method of claim 8, wherein each of at least some annotationinformation of said database is extraversion.
 13. The method of claim 8,wherein each of at least some annotation information of said database isagreeableness.
 14. The method of claim 8, wherein each of at least someannotation information of said database is neuroticism.
 15. The methodof claim 8, further comprising predicting an outcome of a psychologicaltest for the subject, based on said extracted annotation information.